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1 Oracle Data Mining Concepts 10g Release 2 (10.2) B June 2005

2 Oracle Data Mining Concepts, 10g Release 2 (10.2) B Copyright 2005, Oracle. All rights reserved. Primary Authors: Margaret Taft, Ramkumar Krishnan, Mark Hornick, Denis Muhkin, George Tang, Shiby Thomas, Peter Stengard Contributing Authors: Charlie Berger, Marcos Campos, Boriana Milenova, Sunil Venkayla Contributor: Robert Haberstroh The Programs (which include both the software and documentation) contain proprietary information; they are provided under a license agreement containing restrictions on use and disclosure and are also protected by copyright, patent, and other intellectual and industrial property laws. Reverse engineering, disassembly, or decompilation of the Programs, except to the extent required to obtain interoperability with other independently created software or as specified by law, is prohibited. The information contained in this document is subject to change without notice. If you find any problems in the documentation, please report them to us in writing. This document is not warranted to be error-free. Except as may be expressly permitted in your license agreement for these Programs, no part of these Programs may be reproduced or transmitted in any form or by any means, electronic or mechanical, for any purpose. If the Programs are delivered to the United States Government or anyone licensing or using the Programs on behalf of the United States Government, the following notice is applicable: U.S. GOVERNMENT RIGHTS Programs, software, databases, and related documentation and technical data delivered to U.S. Government customers are "commercial computer software" or "commercial technical data" pursuant to the applicable Federal Acquisition Regulation and agency-specific supplemental regulations. As such, use, duplication, disclosure, modification, and adaptation of the Programs, including documentation and technical data, shall be subject to the licensing restrictions set forth in the applicable Oracle license agreement, and, to the extent applicable, the additional rights set forth in FAR , Commercial Computer Software Restricted Rights (June 1987). Oracle Corporation, 500 Oracle Parkway, Redwood City, CA The Programs are not intended for use in any nuclear, aviation, mass transit, medical, or other inherently dangerous applications. It shall be the licensee's responsibility to take all appropriate fail-safe, backup, redundancy and other measures to ensure the safe use of such applications if the Programs are used for such purposes, and we disclaim liability for any damages caused by such use of the Programs. Oracle, JD Edwards, PeopleSoft, and Retek are registered trademarks of Oracle Corporation and/or its affiliates. Other names may be trademarks of their respective owners. The Programs may provide links to Web sites and access to content, products, and services from third parties. Oracle is not responsible for the availability of, or any content provided on, third-party Web sites. You bear all risks associated with the use of such content. If you choose to purchase any products or services from a third party, the relationship is directly between you and the third party. Oracle is not responsible for: (a) the quality of third-party products or services; or (b) fulfilling any of the terms of the agreement with the third party, including delivery of products or services and warranty obligations related to purchased products or services. Oracle is not responsible for any loss or damage of any sort that you may incur from dealing with any third party.

3 Contents Preface... vii Intended Audience... Documentation Accessibility... Related Documents... Conventions... vii vii viii viii 1 Introduction to Oracle Data Mining What is Data Mining? What Is Data Mining in the Database? What Is Oracle Data Mining? Data Mining Functions New Features Data for Oracle Data Mining Data, Cases, and Attributes Data Requirements ODM Data Table Format Column Data Types Supported by ODM Nested Columns in ODM Missing Values Missing Values and NULL Values in ODM Missing Value Handling Sparse Data Outliers and Oracle Data Mining Data Preparation Winsorizing and Trimming Binning (Discretization) Methods for Computing Bin Boundaries Normalization Supervised Data Mining Classification Algorithms for Classification Decision Tree Algorithm Decision Tree Rules iii

4 XML for Decision Tree Models Naive Bayes Algorithm Adaptive Bayes Network Algorithm ABN Model Types ABN Rules Support Vector Machine Algorithm Active Learning Sampling for Classification Automatic Kernel Selection Data Preparation and Settings Choice for Support Vector Machines Data Preparation for Classification Outliers NULL Values Normalization Costs Priors Regression Algorithm for Regression Attribute Importance Data Preparation for Attribute Importance Algorithm for Attribute Importance Minimum Description Length Algorithm Anomaly Detection Algorithm for Anomaly Detection Specify the One-Class SVM Algorithm Testing Supervised Models Confusion Matrix Lift Receiver Operating Characteristics Test Metrics for Regression Models Unsupervised Data Mining Clustering Algorithms for Clustering Enhanced k-means Algorithm Data for k-means Data Preparation for k-means Scoring (Applying Models) Orthogonal Partitioning Clustering (O-Cluster) Algorithm O-Cluster Data Use Binning for O-Cluster O-Cluster Attribute Type O-Cluster Scoring Outliers and Clustering K-Means and O-Cluster Comparison Association Data for Association Models iv

5 Difficult Cases for Associations Finding Associations Involving Rare Events Algorithm for Associations Feature Extraction Algorithm for Feature Extraction NMF for Text Mining Data Preparation for NMF Data Mining Process How Is Data Mining Done? How Does Oracle Data Mining Support Data Mining? Java and PL/SQL Interfaces Automated Data Mining Data Mining Functions Graphical Interfaces Model Deployment Text Mining Using Oracle Data Mining What is Text Mining? Document Classification Combining Text and Structured Data ODM Support for Text Mining Classification and Text Mining Clustering and Text Mining Feature Extraction and Text Mining Association and Text Mining Regression and Text Mining Anomaly Detection and Text Mining Oracle Support for Text Mining Oracle Data Mining Scoring Engine ODM Scoring Engine Features ODM Scoring Engine Installation Scoring in Data Mining Applications Moving Data Mining Models Using the Oracle Data Mining Scoring Engine Sequence Similarity Search and Alignment (BLAST) Bioinformatics Sequence Search and Alignment BLAST in the Oracle Database Oracle Data Mining Sequence Search and Alignment Capabilities Glossary Index v

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7 Preface This manual discusses the basic concepts underlying Oracle Data Mining (ODM). Details of programming with the Java and PL/SQL interfaces are discussed in the Oracle Data Mining Application Developer's Guide. Intended Audience This manual is intended for anyone planning to use any of the ODM interfaces to do data mining in an Oracle Database. ODM has a graphical user interface (Oracle Data Miner) and programmatic interfaces for Java and PL/SQL. Documentation Accessibility Our goal is to make Oracle products, services, and supporting documentation accessible, with good usability, to the disabled community. To that end, our documentation includes features that make information available to users of assistive technology. This documentation is available in HTML format, and contains markup to facilitate access by the disabled community. Accessibility standards will continue to evolve over time, and Oracle is actively engaged with other market-leading technology vendors to address technical obstacles so that our documentation can be accessible to all of our customers. For more information, visit the Oracle Accessibility Program Web site at Accessibility of Code Examples in Documentation Screen readers may not always correctly read the code examples in this document. The conventions for writing code require that closing braces should appear on an otherwise empty line; however, some screen readers may not always read a line of text that consists solely of a bracket or brace. Accessibility of Links to External Web Sites in Documentation This documentation may contain links to Web sites of other companies or organizations that Oracle does not own or control. Oracle neither evaluates nor makes any representations regarding the accessibility of these Web sites. TTY Access to Oracle Support Services Oracle provides dedicated Text Telephone (TTY) access to Oracle Support Services within the United States of America 24 hours a day, seven days a week. For TTY support, call vii

8 Related Documents The documentation set for Oracle Data Mining is part of the Oracle Database Documentation Library 10g Release 2 (10.2). The ODM documentation set consists of the following documents, available online: Oracle Data Mining Administrator's Guide Oracle Data Mining Concepts (this document) Oracle Data Mining Application Developer's Guide For the Javadoc documentation for the JDM-compliant Java interface to Oracle Data Mining, see Oracle Data Mining Java API Reference. For information about the PL/SQL interface for Oracle Data Mining, see the descriptions of the packages DBMS_DATA_MINING, DBMS_DATA_MINING_ TRANSFORM, and DBMS_PREDICTIVE_ANALYTICS in the Oracle Database PL/SQL Packages and Types Reference For information about the Data Mining built-in functions, see the description of the Data Mining Functions in the Oracle Database SQL Reference. Last-minute information about ODM is provided in the platform-specific release notes or README files. For information about Oracle Data Miner, the graphical user interface for ODM, see the online help included with Oracle Data Miner. Oracle Data Miner is distributed on Oracle Technology Network at For information about the data mining process in general, independent of both industry and tool, a good source is the CRISP-DM project (Cross-Industry Standard Process for Data Mining) at For more information about the database underlying Oracle Data Mining, see: Oracle Database 2 Day DBA Oracle Administrator s Guide, Oracle Database 10g Installation Guide for your platform. For information about developing applications to interact with the Oracle Database, see Oracle Application Developer s Guide Fundamentals For information about upgrading from Oracle Data Mining release 10.1, see Oracle Database Upgrade Guide Oracle Data Mining Administrator's Guide Conventions The following text conventions are used in this document: Convention boldface Meaning Boldface type indicates graphical user interface elements associated with an action, or terms defined in text or the glossary. viii

9 Convention italic monospace Meaning Italic type indicates book titles, emphasis, or placeholder variables for which you supply particular values. Monospace type indicates commands within a paragraph, URLs, code in examples, text that appears on the screen, or text that you enter. ix

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11 1 Introduction to Oracle Data Mining This chapter includes the following topics: What is Data Mining? What Is Data Mining in the Database? What Is Oracle Data Mining? New Features What is Data Mining? Too much data and not enough information this is a problem facing many businesses and industries. Most businesses have an enormous amount of data, with a great deal of information hiding within it, but "hiding" is usually exactly what it is doing: So much data exists that it overwhelms traditional methods of data analysis. Data mining provides a way to get at the information buried in the data. Data mining creates models to find hidden patterns in large, complex collections of data, patterns that sometimes elude traditional statistical approaches to analysis because of the large number of attributes, the complexity of patterns, or the difficulty in performing the analysis. What Is Data Mining in the Database? Data mining projects usually require a significant amount of data collection and data processing before and after model building. Data tables are created by combining many different types and sources of information. Real-world data is often dirty, that is, includes wrong or missing values; data must often be cleaned before it can be used. Data is filtered, normalized, sampled, transformed in various ways, and eventually used as input to data mining algorithms. Up to 80% of the effort in a data mining project is often devoted to data preparation. When the data is stored as a table in a database, data preparation can be performed using database facilities. Data mining models have to be built, tested, validated, managed, and deployed in their appropriate application domain environments. The data mining results may need to be post-processed as part of domain specific computations (for example, calculating estimated risks, expected utilities, and response probabilities) and then stored into permanent databases or data warehouses. Making the entire data mining process work in a reproducible and reliable way is challenging; it may involve automation and transfers across servers, data repositories, applications, and tools. For example, some data mining tools require that data be exported from the corporate database and converted to the data mining tool s format; Introduction to Oracle Data Mining 1-1

12 What Is Oracle Data Mining? data mining results must be imported into the database. Removing or reducing these obstacles can enable data mining to be utilized more frequently to extract more valuable information and, in many cases, to make a significant impact on the bottom-line of an enterprise. Data mining in the database makes the data movement required by tools that do not operate in the database unnecessary and make it much easier to mine up-to-date data. Also, the less data movement, the less time the entire data mining process takes. Data movement can make data insecure. If data never leaves the database, database security protects the data. In summary, data mining in the database provides the following benefits: Less data movement More data security Up-to-date data What Is Oracle Data Mining? Data Mining Functions Oracle Data Mining (ODM) embeds data mining within the Oracle database. ODM algorithms operate natively on relational tables or views, thus eliminating the need to extract and transfer data into standalone tools or specialized analytic servers. ODM s integrated architecture results in a simpler, more reliable, and more efficient data management and analysis environment. Data mining tasks can run asynchronously and independently of any specific user interface as part of standard database processing pipelines and applications. Data analysts can mine the data in the database, build models and methodologies, and then turn those results and methodologies into full-fledged application components ready to be deployed in production environments. The benefits of the integration with the database cannot be emphasized enough when it comes to deploying models and scoring data in a production environment. ODM allows a user to take advantage of all aspects of Oracle's technology stack as part of an application. Also, fewer "moving parts" results in a simpler, more reliable, more powerful advanced business intelligence application. ODM provides single-user multi-session access to models. ODM programs can run either asynchronously or synchronously in the Java interface. ODM programs using the PL/SQL interface run synchronously; to run PL/SQL asynchronously requires using the Oracle Scheduler. For a brief description of the ODM interfaces, see "Java and PL/SQL Interfaces" on page 5-2. Data mining functions can be divided into two categories: supervised (directed) and unsupervised (undirected). Supervised functions are used to predict a value; they require the specification of a target (known outcome). Targets are either binary attributes indicating yes/no decisions (buy/don t buy, churn or don t churn, etc.) or multi-class targets indicating a preferred alternative (color of sweater, likely salary range, etc.). Naive Bayes for classification is a supervised mining algorithm. Unsupervised functions are used to find the intrinsic structure, relations, or affinities in data. Unsupervised mining does not use a target. Clustering algorithms can be used to find naturally occurring groups in data. Data mining can also be classified as predictive or descriptive. Predictive data mining constructs one or more models; these models are used to predict outcomes for new 1-2 Oracle Data Mining Concepts

13 New Features New Features data sets. Predictive data mining functions are classification and regression. Naive Bayes is one algorithm used for predictive data mining. Descriptive data mining describes a data set in a concise way and presents interesting characteristics of the data. Descriptive data mining functions are clustering, association models, and feature extraction. k-means clustering is an algorithm used for descriptive data mining. Different algorithms serve different purposes; each algorithm has advantages and disadvantages. A given algorithm can be used to solve different kinds of problems. For example, k-means clustering is unsupervised data mining; however, if you use k-means clustering to assign new records to a cluster, it performs predictive data mining. Similarly, decision tree classification is supervised data mining; however, the decision tree rules can be used for descriptive purposes. Oracle Data Mining supports the following data mining functions: Supervised data mining: Classification: Grouping items into discrete classes and predicting which class an item belongs to Regression: Approximating and forecasting continuous values Attribute Importance: Identifying the attributes that are most important in predicting results Anomaly Detection: Identifying items that do not satisfy the characteristics of "normal" data (outliers) Unsupervised data mining: Clustering: Finding natural groupings in the data Association models: Analyzing "market baskets" Feature extraction: Creating new attributes (features) as a combination of the original attributes Oracle Data Mining permits mining of one or more columns of text data. Oracle Data Mining also supports specialized sequence search and alignment algorithms (BLAST) used to detect similarities between nucleotide and amino acid sequences. The following features are new in Oracle Data Mining 10g, Release 2 (compared with ODM 10g, Release 1): New algorithms and improvements to existing algorithms: Decision Tree algorithm, a fast, scalable means of extracting predictive and descriptive information from a database table with respect to a user-supplied target; Decision Tree provides human-understandable rules One-Class Support Vector Machine algorithm for anomaly detection Active learning, an enhancement to the Support Vector Machine algorithm that provides a way to deal with large build data sets Completely revised Java interface consistent with the Java Data Mining (JDM) standard for data mining (JSR-73) For information about JDM, see the Java Help for the JSR-73 Specification, available on the Oracle Technology Network at Introduction to Oracle Data Mining 1-3

14 New Features SQL Data Mining Functions for applying a model to new data: CLUSTER_ID, CLUSTER_PROBABILITY, CLUSTER_SET, FEATURE_ID,FEATURE_SET, FEATURE_VALUE,PREDICTION, PREDICTION_COST, PREDICTION_DETAILS, PREDICTION_PROBABILITY, and PREDICTION_SET O-cluster algorithm added to PL/SQL interface Oracle Data Miner, a graphical user interface for ODM; Oracle Data Miner is distributed on Oracle Technology Network The PL/SQL package DBMS_PREDICTIVE_ANALYTICS that automates the later stages of data mining; includes the following procedures: EXPLAIN to rank attributes in order of influence in explaining a target column PREDICT to predict the value of a target attribute (categorical or numerical) Oracle Spreadsheet Add-In for Predictive Analytics enables Microsoft Excel users to mine their Oracle Database or Excel data using the automated methodologies provided by DBMS_PREDICTIVE_ANALYTICS; the Add-In is distributed on Oracle Technology Network 1-4 Oracle Data Mining Concepts

15 2 Data for Oracle Data Mining This chapter describes data requirements and how the data should be prepared before it is mined using either of the Oracle Data Mining (ODM) interfaces. The data preparation required depends on the type of model that you plan to build and the characteristics of the data. For example, data with attributes that take on a small number of values, that is, that have low cardinality, may not require binning. In general, users must prepare data before invoking ODM algorithms. The following topics are addressed: Data, Cases, and Attributes Data Requirements Data Preparation Data, Cases, and Attributes Data used by ODM consists of tables or views stored in an Oracle database. Both ordinary tables and nested tables can be used as input data. The data used in a data mining operation is often called a data set. Data has a physical organization and a logical interpretation. Column names refer to physical organization; attribute names, described in the next paragraph, refer to the logical interpretation of the data. The rows of a data table are often called cases, records, or examples. The columns of the data tables are called attributes or fields; each attribute in a record holds a cell of information. Attribute names are constant from record to record for unnested tables; the values in the attributes can vary from record to record. For example, each record may have an attribute labeled "annual income." The value in the annual income attribute can vary from one record to another. ODM distinguishes two types of attributes: categorical and numerical. Categorical attributes are those that define their values as belonging to a small number of discrete categories or classes; there is no implicit order associated with the values. If there are only two possible values, for example, yes and no, or male and female, the attribute is said to be binary. If there are more than two possible values, for example, small, medium, large, extra large, the attribute is said to be multiclass. Numerical attributes are numbers that take on a large number of values that have an order, for example, annual income. For numerical attributes, the differences between values are also ordered. Annual income could theoretically be any value from zero to infinity, though in practice annual income occupies a bounded range and takes on a finite number of values. Data for Oracle Data Mining 2-1

16 Data Requirements Data Requirements ODM Data Table Format You can often transform numerical attributes to categorical attributes. For example, annual income could be divided into three categories: low, medium, high. Conversely, you can explode categorical values to transform them into numerical values. Classification and Regression algorithms require a target attribute. A supervised model can predict a single target attribute. The target attribute for all classification algorithms can be numerical or categorical. The ODM regression algorithm supports only numerical target attributes. Certain ODM algorithms support unstructured text attributes. Although unstructured data includes images, audio, video, geospatial mapping data, and documents or text, ODM supports mining text data only. An input table can contain one or more text columns. ODM supports several types of input data, depending on data table format, column data type, and attribute type. ODM data must reside in a single table or view in an Oracle database. The table or view must be a standard relational table, where each case is represented by one row in the table, with each attribute represented by a column in the table. The columns must be of one of the types supported by ODM. Column Data Types Supported by ODM ODM does not support all the data types that Oracle supports. Each attribute (column) in a data set used by ODM must have one of the following data types: INTEGER NUMBER FLOAT VARCHAR2 CHAR DM_NESTED_NUMERICALS (nested column) DM_NESTED_CATEGORICALS (nested column) The supported attribute data types have a default attribute type (categorical or numerical). For details, see Oracle Data Mining Application Developer's Guide. Nested Columns in ODM Nested table columns can be used for capturing in a single table or view data that is distributed over many tables (for example, a star schema). Nested columns allow you to capture one-to-many relationships (for example, one customers can buy many products). Nested tables are required if the data has more than 1000 attributes; nested tables are useful if the data is sparse, or if the data is already persisted in a transactional format and must be passed to the data mining interface through an object view. 2-2 Oracle Data Mining Concepts

17 Data Requirements Note: The Decision Tree algorithm, described in "Decision Tree Algorithm" on page 3-2, does not support nested columns. Missing Values The fixed collection types DM_NESTED_NUMERICALS and DM_NESTED_ CATEGORICALS are used to define columns that represent collections of numerical attributes and categorical attributes, respectively. For a given case identifier, attribute names must be unique across all the collections and individual columns. The fixed collection types enforce this requirement. Data tables often contain missing values. Missing Values and NULL Values in ODM Certain algorithms assume that a NULL value indicates a missing value; others assume that a NULL value indicates sparse data, as described in "Sparse Data" on page 2-3. Missing Value Handling ODM is robust in handling missing values and does not require users to treat missing values in any special way. ODM will ignore missing values but will use non-missing data in a case. If an algorithm assumes that NULL values indicate sparse data, then you should treat any values that are true missing values. Sparse Data Data is said to be sparse if only a small fraction (no more than 20%, often 3% or less) of the attributes are non-zero or non-null for any given case. Sparse data occurs, for example, in market basket problems. In a grocery store, there might be 10,000 products in the store, and the average size of a basket (the collection of distinct items that a customer purchases in a typical transaction) is on average 50 products. In this example, a transaction (case or record) has on average 50 out of 10,000 attributes that are not null. This implies that the fraction of non-zero attributes in the table (or the density) is approximately 50/10,000, or 0.5%. This density is typical for market basket and text mining problems. Association models are designed to process sparse data; indeed, if the data is not sparse, the algorithm may require a large amount of temporary space or may not be able to build a model. Sparse data is represented in a table in such a way that avoids the specification of the most common value to save storage. In such a specification of sparse data, a missing value is implicitly interpreted as the most common value. Different algorithms make different assumptions about what indicates sparse data. For Support Vector Machine, k-means, association, and Non-Negative Matrix Factorization, NULL values indicate sparse data; for all other algorithms, NULL values indicate missing values. See the description of each algorithm for information about how it interprets NULL values. Data for Oracle Data Mining 2-3

18 Data Preparation Outliers and Oracle Data Mining Data Preparation Winsorizing and Trimming Binning (Discretization) An outlier is a value that is far outside the normal range in a data set, typically a value that is several standard deviations from the mean. The presence of outliers can have a significant impact on certain kinds of ODM models. Naive Bayes, Adaptive Bayes Network, Support Vector Machine, Attribute Importance, either clustering algorithm, and Non-Negative Matrix Factorization are sensitive to outliers. For example, the presence of outliers, when external equal-width binning is used, makes most of the data concentrate in a few bins (a single bin in extreme cases). As a result, the ability of the model to detect differences in numerical attributes may be significantly lessened. For example, a numerical attribute such as income may have all the data belonging to a single bin except for one entry (the outlier) that belongs to a different bin. For outlier treatments, see "Winsorizing and Trimming" on page 2-4. Data is said to be prepared when certain data transformations required by a data mining algorithm are performed by the user before the algorithm is invoked. For most algorithms, data must be prepared before the algorithm is invoked. Different algorithms have different requirements for data preparation; recommended data preparation is discussed with each algorithm in Chapter 3 and Chapter 4. Data preparation can take many forms, such as joining two or more tables so that all required data is in a single table or view, transforming numerical attributes by applying numerical functions to them, recoding attributes, treating missing values, treating outliers, omitting selected columns for a training data set, and so forth. ODM includes transformations that perform the following data-mining-specific transformations: Winsorizing and trimming to treat outliers Discretization to reduce the number of distinct values for an attribute Normalization to convert attribute values to a common range Certain algorithms are sensitive to outliers. Winsorizing and trimming transformations are used to deal with outliers. Winsorizing involves setting the tail values of an attribute to some specified value. For example, for a 90% Winsorization, the bottom 5% of values are set equal to the minimum value in the 6th percentile, while the upper 5% are set equal to the maximum value in the 95th percentile. Trimming removes the tails in the sense that trimmed values are ignored in further computations. This is achieved by setting the tails to NULL. This process is sometimes called clipping. Some ODM algorithms may benefit from binning (discretizing) both numeric and categorical data. Naive Bayes, Adaptive Bayes Network, Clustering, Attribute Importance, and Association Rules algorithms may benefit from binning. 2-4 Oracle Data Mining Concepts

19 Data Preparation Normalization Binning means grouping related values together, thus reducing the number of distinct values for an attribute. Having fewer distinct values typically leads to a more compact model and one that builds faster. Binning must be performed carefully. Proper binning can improve model accuracy; improper binning can lead to loss in accuracy. Methods for Computing Bin Boundaries ODM utilities provide three methods for computing bin boundaries from the data: Top N most frequent items: This technique is used to bin categorical values. The bin definition for each attribute is computed based on the occurrence frequency of values that are computed from the data. The user specifies a particular number of bins, say N. Each of the bins bin_1,..., bin_n corresponds to the values with top frequencies. The bin bin_n+1 corresponds to all remaining values. Equi-Width Binning: This technique is used to bin numerical values. For numerical attributes, ODM finds the minimum (min) and maximum (max) values for every attribute in the data. Then ODM divides the [min, max] range into N equal bins of size d=(max-min)/n. Thus bin 1 is [min, min+d), bin 2 is [min+d, min+2d), and bin N is [min+(n-1)*d,max]. The number of bins can either be specified by the user or calculated by the transformation. Quantile Binning: This technique is used to bin numerical values. The definition for each relevant attribute is computed based on the minimum values for each quantile, where quantiles are computed from the data using NTILE function. Bins bin_1,..., bin_n span the following ranges: bin_1 spans [min_1,min_2]; bin_2,..., bin_i,..., bin_n-1 span (min_i, min_(i+1)] and bin_n spans (min_n, max_n]. Bins with equal left and right boundaries are collapsed. Normalization converts individual numerical attributes so that each attribute s values lie in the same range. Values are converted to be in the range 0.0 to 1.0 or the range -1.0 to 1.0. Normalization ensures that attributes do not receive artificial weighting caused by differences in the ranges that they span. Some algorithms, such as k-means, Support Vector Machine, and Non-Negative Matrix Factorization, benefit from normalization. Data for Oracle Data Mining 2-5

20 Data Preparation 2-6 Oracle Data Mining Concepts

21 3 Supervised Data Mining Classification This chapter describes supervised models; supervised models are sometimes referred to as predictive models. These models predict a target value. The Java and PL/SQL Oracle Data Mining interfaces support the following supervised functions: Classification Regression Attribute Importance Anomaly Detection This chapter also describes Testing Supervised Models Classification of a collection consists of dividing the items that make up the collection into categories or classes. In the context of data mining, classification is done using a model that is built on historical data. The goal of predictive classification is to accurately predict the target class for each record in new data, that is, data that is not in the historical data. A classification task begins with build data (also known as training data) for which the target values (or class assignments) are known. Different classification algorithms use different techniques for finding relations between the predictor attributes values and the target attribute's values in the build data. These relations are summarized in a model; the model can then be applied to new cases with unknown target values to predict target values. A classification model can also be applied to data that was held aside from the training data to compare the predictions to the known target values; such data is also known as test data or evaluation data. The comparison technique is called testing a model, which measures the model's predictive accuracy. The application of a classification model to new data is called applying the model, and the data is called apply data or scoring data. Applying a model to data is often called scoring the data. Classification is used in customer segmentation, business modeling, credit analysis, and many other applications. For example, a credit card company may wish to predict which customers are likely to default on their payments. Customers are divided into two classes: those who default and those who do not default. Each customer corresponds to a case; data for each case might consist of a number of attributes that describe the customer's spending habits, income, demographic attributes, etc. These are the predictor attributes. The target attribute indicates whether or not the customer has defaulted. The build data is used to build a model that predicts whether new customers are likely to default. Supervised Data Mining 3-1

22 Classification Classification problems can have either binary and multiclass targets. Binary targets are those that take on only two values, for example, good credit risk and poor credit risk. Multiclass targets have more than two values, for example, the product purchased (comb or hair brush or hair pin). Multiclass target values are not assumed to exist in an ordered relation to each other, for example, hair brush is not assumed to be greater or less than comb. Classification problems may require the specification of Costs, described on page 3-7 and Priors, described on page 3-7. Algorithms for Classification ODM provides the following algorithms for classification: Decision Tree Algorithm Naive Bayes Algorithm Adaptive Bayes Network Algorithm Support Vector Machine Algorithm Table 3 1 compares several important features of the classification algorithms. Table 3 1 Feature Classification Algorithm Comparison Naive Bayes Adaptive Bayes Network Support Vector Machine Speed Very fast Fast Fast with active learning Accuracy Transparency Missing value interpretation Good in many domains No rules (black box) Good in many domains Rules for Single Feature Build only Significant No rules (black box) Decision Tree Fast Good in many domains Rules Missing value Missing value Sparse data Missing value Decision Tree Algorithm Decision tree rules provide model transparency so that a business user, marketing analyst, or business analyst can understand the basis of the model's predictions, and therefore, be comfortable acting on them and explaining them to others. In addition to transparency, the Decision Tree algorithm provides speed and scalability. The build algorithm scales linearly with the number of predictor attributes and on the order of nlog(n) with the number of rows, n. Scoring is very fast. Both build and apply are parallelized. The Decision Tree algorithm builds models for binary and multi-class targets. It produces accurate and interpretable models with relatively little user intervention required. The Decision Tree algorithm is implemented in such a way as to handle data in the typical data table formats, to have reasonable defaults for splitting and termination criteria, to perform automatic pruning, and to perform automatic handling of missing values. However, it does not distinguish sparse data from missing data. (See "Sparse Data" on page 2-3 for more information.) Users can specify costs and priors. Decision Tree does not support nested tables. 3-2 Oracle Data Mining Concepts

23 Classification Decision Tree Models can be converted to XML. Decision Tree Rules A Decision Tree model always produces rules. Decision tree rules are in the form "IF predictive information THEN target," as in "IF income is greater than $70K and household size is greater than 3 THEN the probability of Churn is " XML for Decision Tree Models You can generate XML representing a decision tree model; the generated XML satisfies the definition specified in the Data Mining Group Predictive Model Markup Language (PMML) version 2.1 specification. The specification is available at Naive Bayes Algorithm The Naive Bayes algorithm (NB) can be used for both binary and multiclass classification problems. NB builds and scores models extremely rapidly; it scales linearly in the number of predictors and rows. NB makes predictions using Bayes Theorem, which derives the probability of a prediction from the underlying evidence. Bayes Theorem states that the probability of event A occurring given that event B has occurred (P(A B)) is proportional to the probability of event B occurring given that event A has occurred multiplied by the probability of event A occurring ((P(B A)P(A)). Naive Bayes makes the assumption that each attribute is conditionally independent of the others, that is, given a particular value of the target, the distribution of each predictor is independent of the other predictors. In practice, this assumption of independence, even when violated, does not degrade the model s predictive accuracy significantly, and makes the difference between a fast, computationally feasible algorithm and an intractable one. Adaptive Bayes Network Algorithm Adaptive Bayes Network (ABN) is an Oracle proprietary algorithm that provides a fast, scalable, non-parametric means of extracting predictive information from data with respect to a target attribute. (Non-parametric statistical techniques avoid assuming that the population is characterized by a family of simple distributional models, such as standard linear regression, where different members of the family are differentiated by a small set of parameters.) ABN, in Single Feature Build mode, can describe the model in the form of human-understandable rules. The rules produced by ABN are one of its main advantages over Naive Bayes. ABN rules provide model transparency so that a business user, marketer, or business analyst can understand the basis of the model's predictions and therefore, be comfortable acting on them and explaining them to others. In addition to rules, ABN provides performance and scalability, which are derived via various user parameters controlling the trade-off of accuracy and build time. ABN predicts binary as well as multiclass targets. ABN can use costs and priors for both building and scoring (see "Costs" on page 3-7 and "Priors" on page 3-7). ABN Model Types An ABN model is an (adaptive conditional independence model that uses the minimum description length principle to construct and prune an array of conditionally independent network features. Each network feature consists of one or Supervised Data Mining 3-3

24 Classification more conditional probability expressions. The collection of network features forms a product model that provides estimates of the target class probabilities. There can be one or more network features. The number and depth of the network features in the model determine the model mode. There are three model modes for ABN: Pruned Naive Bayes (Naive Bayes Build) Simplified decision tree (Single Feature Build) Boosted (Multi Feature Build) Users can select the ABN model type. Rules are available only for Single Feature Build. Each network feature consists of one or more attributes included in a conditional probability expression. An array of single attribute network features is an MDL-pruned Naive Bayes model. A single multi-attribute network feature model is equivalent to a simplified C4.5 decision tree; such a model is simplified in the sense that numerical attributes are binned and treated as categorical. Furthermore, a single predictor is used to split all nodes at a given tree depth. The splits are k-way, where k is the number of unique (binned) values of the splitting predictor. Finally, a collection of multi-attribute network features forms a product model (boosted mode). All three types provide estimates of the target class probabilities. ABN Rules Rules can be extracted from an Adaptive Bayes Network model as compound predicates. Rules form a human-interpretable depiction of the model and include statistics indicating the number of the relevant training data instances in support of the rule. A record apply instance specifies a pathway in a network feature taking the form of a compound predicate. Note: only. Rules are generated for the single feature build model type For example, suppose the feature consists of two training attributes: Age {20-40, 40-60, 60-80} and Income {<=50K, >50K}. A record instance consisting of a person age 25 and income $42K is expressed as IF AGE IN (20-40) and INCOME IN (<=50K) Suppose that the associated target (for example, response to a promotion) probabilities are {0.8 (no), 0.2 (yes)}. Then we have a detailed rule of the form IF AGE IN (20-40) and INCOME IN (<=50K) THEN Probability = {0.8, 0.2} In addition to the probability distribution, there are the associated training data counts, e.g. {400, 100}. Suppose there is a cost matrix specifying that it is 6 times more costly to predict a no incorrectly than it is to predict a yes incorrectly. Then the cost of predicting yes for this instance is 0.8 * 1 = 0.8 (because the model is wrong in this prediction 80% of the time) and the cost of predicting no is 0.2 * 6 = 1.2. Thus, the minimum cost (best) prediction is yes. Without the cost matrix, the decision is reversed. Implicitly, all errors are equal and we have: 0.8 * 1 = 0.8 for yes and 0.2 * 1 = 0.2 for no. The order of the predicates in the generated rules implies relative importance. When you apply an ABN model for which rules were generated, with a single feature, you get the same result that you would get if you wrote an external program that applied the rules. 3-4 Oracle Data Mining Concepts

25 Classification Support Vector Machine Algorithm Support Vector Machine (SVM) is a state-of-the-art classification and regression algorithm. SVM is an algorithm with strong regularization properties, that is, the optimization procedure maximizes predictive accuracy while automatically avoiding over-fitting of the training data. Neural networks and radial basis functions, both popular data mining techniques, have the same functional form as SVM models; however, neither of these algorithms has the well-founded theoretical approach to regularization that forms the basis of SVM. SVM projects the input data into a kernel space. Then it builds a linear model in this kernel space. A classification SVM model attempts to separate the target classes with the widest possible margin. A regression SVM model tries to find a continuous function such that maximum number of data points lie within an epsilon-wide tube around it. Different types of kernels and different kernel parameter choices can produce a variety of decision boundaries (classification) or function approximators (regression). The ODM SVM implementation supports two types of kernels: linear and Gaussian. ODM also provides automatic parameter estimation on the basis of the characteristics of the data. SVM performs well with real-world applications such as classifying text, recognizing hand-written characters, classifying images, as well as bioinformatics and biosequence analysis. The introduction of SVM in the early 1990s led to an explosion of applications and deepening theoretical analysis that established SVM along with neural networks as one of the standard tools for machine learning and data mining. There is no upper limit on the number of attributes and target cardinality for SVMs; the only constraints are those imposed by hardware. SVM is the preferred algorithm for sparse data. The following new features have been added to the SVM algorithm in ODM 10g Release 2: SVM can also be used to identify novel or anomalous patterns using one-class SVM. For more information, see "Anomaly Detection" on page 3-9. SVM supports active learning. For more information, see "Active Learning" on page 3-5. SVM automatically creates stratified samples for large training sets (see "Sampling for Classification" on page 3-6) and automatically chooses a kernel type for model build (see "Automatic Kernel Selection" on page 3-6). Active Learning SVM models grow as the size of the training data set increases. This property limits SVM models to small and medium size training sets (less than 100,000 cases). Active learning provides a way to deal with large training sets. The termination criteria for active learning is usually an upper bound on the number of support vectors; when the upper bound is attained, the build stops. Alternatively, stopping criteria are qualitative, such as no significant improvement in model accuracy on a held-aside sample. Active learning forces the SVM algorithm to restrict learning to the most informative training examples and not to attempt to use the entire body of data. In most cases, the resulting models have predictive accuracy comparable to that of the standard (exact) SVM model. Active learning can be applied to all SVM models (classification, regression, and one-class). Active learning is on by default. It can be turned off. Supervised Data Mining 3-5

26 Classification Sampling for Classification For classification, SVM automatically performs stratified sampling during model build. The algorithm scans the entire build data set and selects a sample that is balanced across target values. Automatic Kernel Selection SVM automatically determines the appropriate kernel type based on build data characteristics. This selection can be overridden by explicitly specifying a kernel type. Data Preparation and Settings Choice for Support Vector Machines You can influence both the Support Vector Machine (SVM) model quality (accuracy) and performance (build time) through two basic mechanisms: data preparation and model settings. Significant performance degradation can be caused by a poor choice of settings or inappropriate data preparation. Poor settings choices can also lead to inaccurate models. For detailed information about data preparation for SVM models, see the Oracle Data Mining Application Developer's Guide. SVM has built-in mechanisms that attempt to choose appropriate settings automatically based on the data provided. You may need to override the system-determined settings for some domains. Data Preparation for Classification This section summarizes data preparation that may be required by classification algorithms. Outliers Outliers affect classification algorithms as follows: Naive Bayes and Adaptive Bayes Network: The presence of outliers, when external equal-width binning is used, makes most of the data concentrate in a few bins (a single bin in extreme cases). As a result, the discriminating power of these algorithms may be significantly reduced. In this case, quantile binning helps to overcome these problems. Support Vector Machine: The presence of outliers can significantly impact models. Use a clipping transformation to avoid the problems caused by outliers. Decision Tree: The presence of outliers does not impact decision tree models. NULL Values The meaning of NULL values and how to treat them depends on the algorithm as follows: Support Vector Machine: NULL values indicate sparse data. Missing values are not automatically handled. If the data is not sparse and the values are indeed missing at random, it is necessary to perform missing data imputation (that is, perform some kind of missing values treatment) and substitute a non-null value for the NULL value. One simple approach is to use the mean for numerical attributes and the mode for categorical attributes. If you do not treat missing values, the algorithm will not handle the data correctly. For all other classification algorithms, NULL values indicate missing values: Decision Tree, Naive Bayes, and Adaptive Bayes Network: Missing values are handled automatically. 3-6 Oracle Data Mining Concepts

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